Experiment with DDA
Removing Outliers
## [1] "Outliers : 135499aaw, 9l7s14ocz, 9l7s14ocz, g6m2iu73e, lpc2zjkex, srn0c21wi"## Empty data.table (0 rows) of 1 col: IDjoueur
## Empty data.table (0 rows) of 1 col: IDjoueur
## Empty data.table (0 rows) of 1 col: IDjoueur
## [1] "Outliers : at13n1mb2, srn0c21wi"## Empty data.table (0 rows) of 1 col: IDjoueur
## Empty data.table (0 rows) of 1 col: IDjoueur## Empty data.table (0 rows) of 1 col: IDjoueur
## [1] "Outliers : 2lvqyyzt9, 3t1l09dyk, e0tdz7cvh"
## Empty data.table (0 rows) of 1 col: IDjoueur
## Empty data.table (0 rows) of 1 col: IDjoueur
## Empty data.table (0 rows) of 1 col: IDjoueur## [1] "Total number of outliers: 9"## [1] "Total number of outliers motor task: 1"## [1] "Total number of outliers perceptive task: 3"## [1] "Total number of outliers logical task: 6"Modeling objective difficulty for motor task
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: perdant ~ difficulty + timeNorm + (1 | IDjoueur)
## Data: DT
##
## AIC BIC logLik deviance df.resid
## 3151.9 3174.9 -1571.9 3143.9 2336
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.9957 -0.8530 -0.5675 0.9526 2.1623
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 0.6875 0.8292
## Number of obs: 2340, groups: IDjoueur, 78
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.1106 0.1603 -6.928 4.27e-12 ***
## difficulty 3.2297 0.3438 9.395 < 2e-16 ***
## timeNorm -0.4618 0.1650 -2.798 0.00514 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) dffclt
## difficulty -0.585
## timeNorm -0.216 -0.401## The result is correct only if all data used by the model has not changed since model was fitted.
## The result is correct only if all data used by the model has not changed since model was fitted.##
## Logique2 Motrice Sensoriel
## 0 2340 0
## [1] "Player levels from ranef:"
## (Intercept)
## Min. :-2.126430
## 1st Qu.:-0.347466
## Median : 0.049837
## Mean : 0.003376
## 3rd Qu.: 0.448296
## Max. : 1.960052
## [1] "Intercept: -1.11 4.3e-12 ***"
## [1] "Difficulty: 3.23 5.7e-21 ***"
## [1] "Time: -0.462 0.0051 **"
## [1] "R2 fixed: 0.13"
## [1] "R2 mixed: 0.28"
## [1] "Cross Val: 0.61"
## [1] "AIC: 3200"## 0% 25% 50% 75% 100%
## -1.96005180 -0.44829605 -0.04983725 0.34746630 2.12642981
## 0% 25% 50% 75% 100%
## -1.96005180 -0.44829605 -0.04983725 0.34746630 2.12642981
## `geom_smooth()` using method = 'gam'
Modeling objective difficulty for sensory task
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: perdant ~ difficulty + timeNorm + (1 | IDjoueur)
## Data: DT
##
## AIC BIC logLik deviance df.resid
## 2532.7 2555.3 -1262.3 2524.7 2126
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.5031 -0.7242 -0.3428 0.8058 3.7576
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 0.5808 0.7621
## Number of obs: 2130, groups: IDjoueur, 71
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.1935 0.1751 -12.529 <2e-16 ***
## difficulty 9.0967 0.5527 16.459 <2e-16 ***
## timeNorm -0.3650 0.1824 -2.001 0.0454 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) dffclt
## difficulty -0.615
## timeNorm -0.317 -0.299## The result is correct only if all data used by the model has not changed since model was fitted.
## The result is correct only if all data used by the model has not changed since model was fitted.##
## Logique2 Motrice Sensoriel
## 0 0 2130
## [1] "Player levels from ranef:"
## (Intercept)
## Min. :-1.476587
## 1st Qu.:-0.370451
## Median :-0.036883
## Mean : 0.003092
## 3rd Qu.: 0.449790
## Max. : 1.689450
## [1] "Intercept: -2.19 5.2e-36 ***"
## [1] "Difficulty: 9.1 7.2e-61 ***"
## [1] "Time: -0.365 0.045 *"
## [1] "R2 fixed: 0.31"
## [1] "R2 mixed: 0.42"
## [1] "Cross Val: 0.68"
## [1] "AIC: 2500"## 0% 25% 50% 75% 100%
## -1.68944968 -0.44979001 0.03688316 0.37045061 1.47658650
## 0% 25% 50% 75% 100%
## -1.68944968 -0.44979001 0.03688316 0.37045061 1.47658650
## `geom_smooth()` using method = 'gam'
Modeling objective difficulty for logical task
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: perdant ~ difficulty + timeNorm + (1 | IDjoueur)
## Data: DT
##
## AIC BIC logLik deviance df.resid
## 2839.0 2861.8 -1415.5 2831.0 2216
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.7599 -0.7933 -0.4388 0.8894 5.9937
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 1.538 1.24
## Number of obs: 2220, groups: IDjoueur, 74
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.6820 0.1962 -8.574 < 2e-16 ***
## difficulty 4.8761 0.3902 12.497 < 2e-16 ***
## timeNorm -1.0018 0.2129 -4.707 2.52e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) dffclt
## difficulty -0.435
## timeNorm -0.095 -0.617## The result is correct only if all data used by the model has not changed since model was fitted.
## The result is correct only if all data used by the model has not changed since model was fitted.##
## Logique2 Motrice Sensoriel
## 2220 0 0
## [1] "Player levels from ranef:"
## (Intercept)
## Min. :-2.40565
## 1st Qu.:-0.91465
## Median :-0.21046
## Mean : 0.00762
## 3rd Qu.: 1.01654
## Max. : 2.33338
## [1] "Intercept: -1.68 1e-17 ***"
## [1] "Difficulty: 4.88 7.8e-36 ***"
## [1] "Time: -1 2.5e-06 ***"
## [1] "R2 fixed: 0.25"
## [1] "R2 mixed: 0.49"
## [1] "Cross Val: 0.65"
## [1] "AIC: 2800"## 0% 25% 50% 75% 100%
## -2.3333793 -1.0165435 0.2104629 0.9146509 2.4056548
## 0% 25% 50% 75% 100%
## -2.3333793 -1.0165435 0.2104629 0.9146509 2.4056548
## `geom_smooth()` using method = 'gam'
Influence of Player Profiles
Player profiles
Influence of Player Profiles
Objective level and player profile
Playing video games in general and level for each task
Playing board games in general and level for each task
Self efficacy and level for each task
## [1] "self.eff.on.level.m 0.26 0.0019 **"## [1] "self.eff.on.level.l 0.17 0.048 *"Risk aversion and level for each task
## [1] "risk.av.on.level.s 0.29 0.00094 ***"## [1] "risk.av.on.level.l 0.28 0.00098 ***"Age and level for each task
Sex and level for each task
## [1] "sexe.on.level.m -0.39 2.6e-05 ***"## [1] "sexe.on.level.l -0.25 0.0098 **"## [1] "sexe.on.level.m.2 -0.68 2.6e-05 *** mean(A): 0.2 mean(B): -0.56"## [1] "sexe.on.level.l.2 -0.82 0.0099 ** mean(A): 0.2 mean(B): -0.54"Subjective difficulty and play habits
Playing video game in general and subjective difficulty error
Playing board game in general and subjective difficulty error
In game level and subjective difficulty error
Sex and subjective difficulty error
Risk aversion and subjective difficulty error
Self efficacy and subjective difficulty error
OLD!! We investigate the link between player’s reported game habits, feeling of self efficacy, risk aversion and player’s behavior in the different games. Feeling of self efficacy shows a small link with performance on motor task (Kendal \(\tau\)=0.26, p<0.01) and logical task (Kendal \(\tau\)=0.17, p=0.053). Aversion to risk shows a small link with performance on sensory (Kendal \(\tau\)=0.29, p<0.001) and logical task (Kendal \(\tau\)=0.27 p<0.01). In this experiment, female players tend to have a lower performance on motor (Kendal \(\tau\)=-0.4, p<0.001) and logical tasks (Kendal \(\tau\)=-0.25, p<0.01). Player’s sex is also slightly related to the error between subjective and objective difficulty (Kendal \(\tau\)=-0.19, p=0.053) i.e. compared to male players, female players tend to underestimate logical task difficulty.
Influence of Objective difficulty on Subjective Difficulty
All tasks
## [1] "all"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 -0.0310 27 3e-04 ***
## 2: 0.09375 -0.0220 59 0.035 *
## 3: 0.15625 -0.0250 68 0.32 :(
## 4: 0.21875 -0.0045 72 0.75 :(
## 5: 0.28125 -0.0260 77 0.29 :(
## 6: 0.34375 -0.0290 80 0.23 :(
## 7: 0.40625 -0.0340 80 0.18 :(
## 8: 0.46875 -0.0750 80 0.0067 **
## 9: 0.53125 -0.1200 80 4.8e-06 ***
## 10: 0.59375 -0.1300 78 4.8e-06 ***
## 11: 0.65625 -0.2200 79 2.4e-09 ***
## 12: 0.71875 -0.2200 76 3.1e-09 ***
## 13: 0.78125 -0.2500 51 9e-08 ***
## 14: 0.84375 -0.2400 56 3.3e-06 ***
## 15: 0.90625 -0.1900 30 1.2e-06 ***
## 16: 0.96875 -0.2300 17 0.00023 ***
## [1] "good"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 -0.0310 22 0.003 **
## 2: 0.09375 -0.0045 30 0.93 :(
## 3: 0.15625 0.0045 32 0.86 :(
## 4: 0.21875 0.0210 35 0.65 :(
## 5: 0.28125 0.0480 34 0.16 :(
## 6: 0.34375 -0.0200 33 0.73 :(
## 7: 0.40625 -0.0430 34 0.22 :(
## 8: 0.46875 -0.0880 34 0.073 .
## 9: 0.53125 -0.1200 33 0.017 *
## 10: 0.59375 -0.1700 33 0.0027 **
## 11: 0.65625 -0.2100 32 0.0018 **
## 12: 0.71875 -0.2200 26 0.0034 **
## 13: 0.78125 -0.2100 14 0.032 *
## 14: 0.84375 -0.3100 9 0.024 *
## 15: 0.90625 -0.1900 6 0.036 *
## 16: 0.96875 NA 4 NA## Warning: Removed 1 rows containing missing values (geom_point).## Warning: Removed 1 rows containing missing values (geom_errorbar).
## [1] "medium"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 NA 7 NA
## 2: 0.09375 -0.094 44 0.00048 ***
## 3: 0.15625 -0.044 42 0.13 :(
## 4: 0.21875 -0.076 48 0.081 .
## 5: 0.28125 -0.067 53 0.05 .
## 6: 0.34375 -0.031 59 0.21 :(
## 7: 0.40625 -0.038 60 0.33 :(
## 8: 0.46875 -0.077 55 0.039 *
## 9: 0.53125 -0.120 57 0.0017 **
## 10: 0.59375 -0.150 55 0.0031 **
## 11: 0.65625 -0.230 55 2.1e-05 ***
## 12: 0.71875 -0.190 50 0.00014 ***
## 13: 0.78125 -0.210 33 4.1e-05 ***
## 14: 0.84375 -0.240 30 0.00025 ***
## 15: 0.90625 -0.160 17 0.00023 ***
## 16: 0.96875 -0.330 9 0.008 **## Warning: Removed 1 rows containing missing values (geom_point).## Warning: Removed 1 rows containing missing values (geom_errorbar).
## [1] "bad"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 NA 0 NA
## 2: 0.09375 NA 7 NA
## 3: 0.15625 -0.057 23 0.092 .
## 4: 0.21875 -0.076 21 0.25 :(
## 5: 0.28125 -0.067 35 0.084 .
## 6: 0.34375 -0.076 37 0.063 .
## 7: 0.40625 -0.061 39 0.13 :(
## 8: 0.46875 -0.120 40 0.006 **
## 9: 0.53125 -0.100 40 0.0086 **
## 10: 0.59375 -0.180 39 2e-04 ***
## 11: 0.65625 -0.230 36 1.5e-05 ***
## 12: 0.71875 -0.250 34 8.2e-05 ***
## 13: 0.78125 -0.280 19 0.00083 ***
## 14: 0.84375 -0.220 20 0.14 :(
## 15: 0.90625 -0.220 7 0.02 *
## 16: 0.96875 -0.160 4 0.098 .## Warning: Removed 2 rows containing missing values (geom_point).## Warning: Removed 2 rows containing missing values (geom_errorbar).
Motor task
## [1] "all"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 NA 0 NA
## 2: 0.09375 NA 3 NA
## 3: 0.15625 -0.0130 13 0.78 :(
## 4: 0.21875 -0.0045 29 0.21 :(
## 5: 0.28125 -0.0310 61 0.5 :(
## 6: 0.34375 -0.0720 73 0.025 *
## 7: 0.40625 -0.0810 74 0.025 *
## 8: 0.46875 -0.1000 74 0.0053 **
## 9: 0.53125 -0.1300 76 0.00052 ***
## 10: 0.59375 -0.2000 72 2.8e-06 ***
## 11: 0.65625 -0.2500 61 2.2e-06 ***
## 12: 0.71875 -0.2900 38 1.6e-05 ***
## 13: 0.78125 -0.4100 11 0.0065 **
## 14: 0.84375 -0.5400 4 0.2 :(
## 15: 0.90625 NA 0 NA
## 16: 0.96875 NA 0 NA## Warning: Removed 4 rows containing missing values (geom_point).## Warning: Removed 4 rows containing missing values (geom_errorbar).
## [1] "good"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 NA 0 NA
## 2: 0.09375 NA 3 NA
## 3: 0.15625 -0.013 10 0.76 :(
## 4: 0.21875 0.019 18 0.79 :(
## 5: 0.28125 0.076 19 0.095 .
## 6: 0.34375 0.031 19 0.59 :(
## 7: 0.40625 -0.035 19 0.56 :(
## 8: 0.46875 -0.064 19 0.36 :(
## 9: 0.53125 -0.100 18 0.12 :(
## 10: 0.59375 -0.130 17 0.072 .
## 11: 0.65625 -0.220 12 0.054 .
## 12: 0.71875 -0.230 7 0.55 :(
## 13: 0.78125 NA 1 NA
## 14: 0.84375 NA 0 NA
## 15: 0.90625 NA 0 NA
## 16: 0.96875 NA 0 NA## Warning: Removed 6 rows containing missing values (geom_point).## Warning: Removed 6 rows containing missing values (geom_errorbar).
## [1] "medium"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 NA 0 NA
## 2: 0.09375 NA 0 NA
## 3: 0.15625 NA 3 NA
## 4: 0.21875 -0.220 11 0.07 .
## 5: 0.28125 -0.055 33 0.63 :(
## 6: 0.34375 -0.068 38 0.13 :(
## 7: 0.40625 -0.094 38 0.051 .
## 8: 0.46875 -0.110 38 0.036 *
## 9: 0.53125 -0.120 38 0.045 *
## 10: 0.59375 -0.240 34 0.0013 **
## 11: 0.65625 -0.250 33 0.0016 **
## 12: 0.71875 -0.360 21 0.00068 ***
## 13: 0.78125 -0.410 6 0.031 *
## 14: 0.84375 NA 1 NA
## 15: 0.90625 NA 0 NA
## 16: 0.96875 NA 0 NA## Warning: Removed 6 rows containing missing values (geom_point).## Warning: Removed 6 rows containing missing values (geom_errorbar).
## [1] "bad"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 NA 0 NA
## 2: 0.09375 NA 0 NA
## 3: 0.15625 NA 0 NA
## 4: 0.21875 NA 0 NA
## 5: 0.28125 -0.210 9 0.02 *
## 6: 0.34375 -0.200 16 0.0059 **
## 7: 0.40625 -0.077 17 0.26 :(
## 8: 0.46875 -0.130 17 0.13 :(
## 9: 0.53125 -0.170 20 0.029 *
## 10: 0.59375 -0.240 21 0.0039 **
## 11: 0.65625 -0.330 16 0.004 **
## 12: 0.71875 -0.360 10 0.014 *
## 13: 0.78125 -0.570 4 0.095 .
## 14: 0.84375 NA 3 NA
## 15: 0.90625 NA 0 NA
## 16: 0.96875 NA 0 NA## Warning: Removed 7 rows containing missing values (geom_point).## Warning: Removed 7 rows containing missing values (geom_errorbar).
Sensory task
## [1] "all"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 NA 15 NA
## 2: 0.09375 -0.094 54 5.7e-07 ***
## 3: 0.15625 -0.095 61 1.7e-06 ***
## 4: 0.21875 -0.100 43 0.00062 ***
## 5: 0.28125 -0.094 51 0.017 *
## 6: 0.34375 -0.120 46 0.0058 **
## 7: 0.40625 -0.049 41 0.44 :(
## 8: 0.46875 -0.110 45 0.0057 **
## 9: 0.53125 -0.150 48 0.00038 ***
## 10: 0.59375 -0.079 37 0.094 .
## 11: 0.65625 -0.140 40 0.0036 **
## 12: 0.71875 -0.180 53 4.8e-05 ***
## 13: 0.78125 -0.140 33 0.00023 ***
## 14: 0.84375 -0.150 44 0.0018 **
## 15: 0.90625 -0.160 29 1.7e-06 ***
## 16: 0.96875 -0.230 17 0.00023 ***## Warning: Removed 1 rows containing missing values (geom_point).## Warning: Removed 1 rows containing missing values (geom_errorbar).
## [1] "good"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 NA 9 NA
## 2: 0.09375 -0.094 9 0.13 :(
## 3: 0.15625 NA 9 NA
## 4: 0.21875 -0.160 5 0.054 .
## 5: 0.28125 -0.067 5 0.31 :(
## 6: 0.34375 -0.085 4 0.38 :(
## 7: 0.40625 -0.049 5 1 :(
## 8: 0.46875 -0.100 5 0.81 :(
## 9: 0.53125 -0.200 5 0.44 :(
## 10: 0.59375 -0.097 4 0.88 :(
## 11: 0.65625 -0.013 6 1 :(
## 12: 0.71875 -0.270 7 0.075 .
## 13: 0.78125 -0.085 4 0.58 :(
## 14: 0.84375 -0.200 6 0.14 :(
## 15: 0.90625 -0.190 6 0.036 *
## 16: 0.96875 NA 4 NA## Warning: Removed 3 rows containing missing values (geom_point).## Warning: Removed 3 rows containing missing values (geom_errorbar).
## [1] "medium"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 NA 6 NA
## 2: 0.09375 -0.094 38 2.2e-06 ***
## 3: 0.15625 -0.110 32 0.00024 ***
## 4: 0.21875 -0.150 24 0.00073 ***
## 5: 0.28125 -0.110 25 0.036 *
## 6: 0.34375 -0.094 27 0.055 .
## 7: 0.40625 -0.085 24 0.27 :(
## 8: 0.46875 -0.120 22 0.055 .
## 9: 0.53125 -0.180 27 0.0032 **
## 10: 0.59375 -0.130 22 0.038 *
## 11: 0.65625 -0.270 20 0.00072 ***
## 12: 0.71875 -0.170 26 0.0041 **
## 13: 0.78125 -0.210 21 0.00093 ***
## 14: 0.84375 -0.130 23 0.0046 **
## 15: 0.90625 -0.120 16 0.00033 ***
## 16: 0.96875 -0.330 9 0.008 **## Warning: Removed 1 rows containing missing values (geom_point).## Warning: Removed 1 rows containing missing values (geom_errorbar).
## [1] "bad"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 NA 0 NA
## 2: 0.09375 NA 7 NA
## 3: 0.15625 -0.0540 20 0.12 :(
## 4: 0.21875 -0.0044 14 0.95 :(
## 5: 0.28125 -0.0790 21 0.27 :(
## 6: 0.34375 -0.1300 15 0.1 :(
## 7: 0.40625 0.0220 12 0.56 :(
## 8: 0.46875 -0.1300 18 0.081 .
## 9: 0.53125 -0.0870 16 0.093 .
## 10: 0.59375 0.0130 11 0.89 :(
## 11: 0.65625 -0.0130 14 0.66 :(
## 12: 0.71875 -0.2000 20 0.038 *
## 13: 0.78125 -0.1400 8 0.23 :(
## 14: 0.84375 -0.1700 15 0.79 :(
## 15: 0.90625 -0.2200 7 0.02 *
## 16: 0.96875 -0.1600 4 0.098 .## Warning: Removed 2 rows containing missing values (geom_point).## Warning: Removed 2 rows containing missing values (geom_errorbar).
Logical task
## [1] "all"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 -0.031 17 0.027 *
## 2: 0.09375 0.025 38 0.69 :(
## 3: 0.15625 0.058 51 0.15 :(
## 4: 0.21875 0.067 61 0.17 :(
## 5: 0.28125 0.040 66 0.11 :(
## 6: 0.34375 0.034 71 0.4 :(
## 7: 0.40625 0.010 71 0.83 :(
## 8: 0.46875 -0.062 72 0.054 .
## 9: 0.53125 -0.091 73 0.014 *
## 10: 0.59375 -0.130 67 0.0016 **
## 11: 0.65625 -0.230 63 4.3e-07 ***
## 12: 0.71875 -0.210 52 1.7e-05 ***
## 13: 0.78125 -0.280 30 0.00012 ***
## 14: 0.84375 -0.420 14 0.0012 **
## 15: 0.90625 NA 1 NA
## 16: 0.96875 NA 0 NA## Warning: Removed 2 rows containing missing values (geom_point).## Warning: Removed 2 rows containing missing values (geom_errorbar).
## [1] "good"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 -0.031 16 0.04 *
## 2: 0.09375 0.025 25 0.76 :(
## 3: 0.15625 0.130 25 0.23 :(
## 4: 0.21875 0.100 25 0.11 :(
## 5: 0.28125 0.040 24 0.47 :(
## 6: 0.34375 -0.058 25 0.48 :(
## 7: 0.40625 -0.049 24 0.38 :(
## 8: 0.46875 -0.110 25 0.021 *
## 9: 0.53125 -0.120 24 0.047 *
## 10: 0.59375 -0.170 23 0.0048 **
## 11: 0.65625 -0.230 22 0.002 **
## 12: 0.71875 -0.290 19 0.002 **
## 13: 0.78125 -0.280 10 0.041 *
## 14: 0.84375 -0.450 4 0.098 .
## 15: 0.90625 NA 0 NA
## 16: 0.96875 NA 0 NA## Warning: Removed 2 rows containing missing values (geom_point).## Warning: Removed 2 rows containing missing values (geom_errorbar).
## [1] "medium"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 NA 1 NA
## 2: 0.09375 0.013 13 0.83 :(
## 3: 0.15625 0.058 22 0.23 :(
## 4: 0.21875 0.140 24 0.083 .
## 5: 0.28125 0.058 24 0.14 :(
## 6: 0.34375 0.150 24 0.038 *
## 7: 0.40625 0.094 23 0.091 .
## 8: 0.46875 0.031 23 0.7 :(
## 9: 0.53125 -0.013 24 0.92 :(
## 10: 0.59375 -0.022 22 0.72 :(
## 11: 0.65625 -0.130 22 0.096 .
## 12: 0.71875 -0.040 19 0.36 :(
## 13: 0.78125 -0.170 11 0.067 .
## 14: 0.84375 -0.430 7 0.034 *
## 15: 0.90625 NA 1 NA
## 16: 0.96875 NA 0 NA## Warning: Removed 3 rows containing missing values (geom_point).## Warning: Removed 3 rows containing missing values (geom_errorbar).
## [1] "bad"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 NA 0 NA
## 2: 0.09375 NA 0 NA
## 3: 0.15625 -0.060 4 0.36 :(
## 4: 0.21875 -0.160 12 0.21 :(
## 5: 0.28125 0.033 18 0.51 :(
## 6: 0.34375 -0.010 22 0.82 :(
## 7: 0.40625 -0.013 24 0.68 :(
## 8: 0.46875 -0.088 24 0.079 .
## 9: 0.53125 -0.110 25 0.038 *
## 10: 0.59375 -0.200 22 0.01 *
## 11: 0.65625 -0.290 19 0.00023 ***
## 12: 0.71875 -0.290 14 0.0024 **
## 13: 0.78125 -0.350 9 0.0084 **
## 14: 0.84375 NA 3 NA
## 15: 0.90625 NA 0 NA
## 16: 0.96875 NA 0 NA## Warning: Removed 5 rows containing missing values (geom_point).## Warning: Removed 5 rows containing missing values (geom_errorbar).
Influence of Playtime on Subjective Difficulty Error
For all groups, motor, sensitive and logical
##
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTM)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.47512 -0.32099 0.00198 0.28034 0.62514
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.17124 0.02662 6.432 1.52e-10 ***
## timeNorm 0.01183 0.02348 0.504 0.614
## obj.diff -0.63770 0.05194 -12.277 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.1062912)
##
## Null deviance: 264.48 on 2339 degrees of freedom
## Residual deviance: 248.40 on 2337 degrees of freedom
## AIC: 1400.3
##
## Number of Fisher Scoring iterations: 2
##
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTS)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.73012 -0.23623 -0.06378 0.27512 0.77690
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.03288 0.01832 -1.795 0.0728 .
## timeNorm 0.05464 0.02474 2.209 0.0273 *
## obj.diff -0.23453 0.03151 -7.443 1.43e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.1028656)
##
## Null deviance: 224.52 on 2129 degrees of freedom
## Residual deviance: 218.80 on 2127 degrees of freedom
## AIC: 1205.3
##
## Number of Fisher Scoring iterations: 2
##
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTL)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.57929 -0.31805 -0.00183 0.31692 0.63841
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.17798 0.01962 9.072 < 2e-16 ***
## timeNorm 0.07804 0.02588 3.016 0.00259 **
## obj.diff -0.60382 0.04194 -14.398 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.1105958)
##
## Null deviance: 268.47 on 2219 degrees of freedom
## Residual deviance: 245.19 on 2217 degrees of freedom
## AIC: 1416.9
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.2887668 0.4250930 -0.13953917 234 8.3e-09 ***
## 2: 4.5 0.3577534 0.4490995 -0.09225023 234 0.00012 ***
## 3: 7.5 0.3736264 0.4588281 -0.08415455 234 0.0011 **
## 4: 10.5 0.3443223 0.4590774 -0.11697149 234 1.4e-06 ***
## 5: 13.5 0.3388278 0.4603425 -0.12311547 234 1.6e-07 ***
## 6: 16.5 0.3504274 0.4656950 -0.11694812 234 1.1e-06 ***
## 7: 19.5 0.3284493 0.4694363 -0.14111674 234 1.2e-09 ***
## 8: 22.5 0.3431013 0.4793797 -0.14609825 234 4.7e-09 ***
## 9: 25.5 0.3833944 0.4792794 -0.09957072 234 0.00013 ***
## 10: 28.5 0.3363858 0.4675171 -0.12098146 234 4.9e-08 ***
## time error.diff shapes
## 1: 1.5 -0.13953917 24
## 2: 4.5 -0.09225023 24
## 3: 7.5 -0.08415455 24
## 4: 10.5 -0.11697149 24
## 5: 13.5 -0.12311547 24
## 6: 16.5 -0.11694812 24
## 7: 19.5 -0.14111674 24
## 8: 22.5 -0.14609825 24
## 9: 25.5 -0.09957072 24
## 10: 28.5 -0.12098146 24
## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.1368209 0.2153795 -0.10692408 213 3.2e-08 ***
## 2: 4.5 0.2964453 0.4306730 -0.15011494 213 3e-09 ***
## 3: 7.5 0.3333333 0.4960787 -0.16696074 213 9.6e-11 ***
## 4: 10.5 0.3521127 0.4942637 -0.14997408 213 5.9e-09 ***
## 5: 13.5 0.3876593 0.4785235 -0.09232594 213 1e-04 ***
## 6: 16.5 0.3970490 0.4910775 -0.09082835 213 0.00014 ***
## 7: 19.5 0.3816231 0.4794597 -0.10563208 213 1.2e-07 ***
## 8: 22.5 0.4084507 0.5148548 -0.10674468 213 8.5e-05 ***
## 9: 25.5 0.4151576 0.4815308 -0.06349500 213 0.011 *
## 10: 28.5 0.3474178 0.4938488 -0.15316819 213 1.4e-08 ***
## time error.diff shapes
## 1: 1.5 -0.10692408 24
## 2: 4.5 -0.15011494 24
## 3: 7.5 -0.16696074 24
## 4: 10.5 -0.14997408 24
## 5: 13.5 -0.09232594 24
## 6: 16.5 -0.09082835 24
## 7: 19.5 -0.10563208 24
## 8: 22.5 -0.10674468 24
## 9: 25.5 -0.06349500 24
## 10: 28.5 -0.15316819 24
## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.2149292 0.2543626 -0.07057268 222 0.0012 **
## 2: 4.5 0.3558559 0.3238165 0.03016309 222 0.24 :(
## 3: 7.5 0.3873874 0.4029150 -0.01604677 222 0.54 :(
## 4: 10.5 0.3925354 0.4338872 -0.03958987 222 0.1 :(
## 5: 13.5 0.4118404 0.4496167 -0.04062829 222 0.11 :(
## 6: 16.5 0.4227799 0.4766409 -0.05128067 222 0.032 *
## 7: 19.5 0.4111969 0.4797636 -0.07613895 222 0.0041 **
## 8: 22.5 0.3880309 0.4387192 -0.05567027 222 0.025 *
## 9: 25.5 0.4182754 0.4588002 -0.04151817 222 0.098 .
## 10: 28.5 0.4427284 0.4780249 -0.03297758 222 0.24 :(
## time error.diff shapes
## 1: 1.5 -0.07057268 24
## 2: 4.5 0.03016309 16
## 3: 7.5 -0.01604677 16
## 4: 10.5 -0.03958987 16
## 5: 13.5 -0.04062829 16
## 6: 16.5 -0.05128067 24
## 7: 19.5 -0.07613895 24
## 8: 22.5 -0.05567027 24
## 9: 25.5 -0.04151817 16
## 10: 28.5 -0.03297758 16
For all taks, per group
##
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTAll[niveau.group ==
## "bad"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.62236 -0.30061 -0.03105 0.30943 0.70258
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.07333 0.02692 2.724 0.0065 **
## timeNorm 0.04162 0.02533 1.643 0.1005
## obj.diff -0.45089 0.04604 -9.794 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.1116794)
##
## Null deviance: 245.47 on 2099 degrees of freedom
## Residual deviance: 234.19 on 2097 degrees of freedom
## AIC: 1361.1
##
## Number of Fisher Scoring iterations: 2
##
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTAll[niveau.group ==
## "medium"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.66633 -0.30168 -0.03745 0.30093 0.70159
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.05934 0.01808 3.282 0.001043 **
## timeNorm 0.07874 0.02174 3.622 0.000297 ***
## obj.diff -0.40644 0.03402 -11.948 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.1130013)
##
## Null deviance: 354.95 on 2999 degrees of freedom
## Residual deviance: 338.66 on 2997 degrees of freedom
## AIC: 1977.6
##
## Number of Fisher Scoring iterations: 2
##
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTAll[niveau.group ==
## "good"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.61482 -0.25581 -0.02273 0.26062 0.68726
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.12404 0.01869 6.637 4.39e-11 ***
## timeNorm -0.03577 0.03222 -1.110 0.267
## obj.diff -0.40591 0.04638 -8.753 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.09536639)
##
## Null deviance: 163.52 on 1589 degrees of freedom
## Residual deviance: 151.35 on 1587 degrees of freedom
## AIC: 780.68
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.2836735 0.4554798 -0.18122716 210 5.2e-11 ***
## 2: 4.5 0.3761905 0.4871346 -0.11327679 210 1.9e-05 ***
## 3: 7.5 0.3857143 0.5001913 -0.11477171 210 1.8e-05 ***
## 4: 10.5 0.3367347 0.4808474 -0.14718789 210 9.8e-09 ***
## 5: 13.5 0.3693878 0.4743164 -0.10640922 210 5e-05 ***
## 6: 16.5 0.3693878 0.4800480 -0.11073776 210 4.5e-06 ***
## 7: 19.5 0.3197279 0.4553800 -0.14906689 210 8.6e-08 ***
## 8: 22.5 0.3591837 0.4524433 -0.09927856 210 0.00016 ***
## 9: 25.5 0.3598639 0.4321020 -0.07754019 210 0.0036 **
## 10: 28.5 0.3598639 0.4649090 -0.10777513 210 5.4e-05 ***
## time error.diff shapes
## 1: 1.5 -0.18122716 24
## 2: 4.5 -0.11327679 24
## 3: 7.5 -0.11477171 24
## 4: 10.5 -0.14718789 24
## 5: 13.5 -0.10640922 24
## 6: 16.5 -0.11073776 24
## 7: 19.5 -0.14906689 24
## 8: 22.5 -0.09927856 24
## 9: 25.5 -0.07754019 24
## 10: 28.5 -0.10777513 24
## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.2123810 0.2820147 -0.09988244 300 4.7e-07 ***
## 2: 4.5 0.3442857 0.4311776 -0.09488366 300 1.4e-05 ***
## 3: 7.5 0.3504762 0.4731184 -0.12644513 300 5.8e-08 ***
## 4: 10.5 0.3680952 0.4748829 -0.11135073 300 9.7e-07 ***
## 5: 13.5 0.3747619 0.4484668 -0.07739952 300 0.00028 ***
## 6: 16.5 0.3961905 0.4665071 -0.06789986 300 0.0013 **
## 7: 19.5 0.4095238 0.4825212 -0.07963820 300 8.8e-05 ***
## 8: 22.5 0.3904762 0.4837553 -0.09664082 300 1.4e-05 ***
## 9: 25.5 0.4385714 0.4854995 -0.04906552 300 0.035 *
## 10: 28.5 0.3847619 0.4690868 -0.08176457 300 5.3e-05 ***
## time error.diff shapes
## 1: 1.5 -0.09988244 24
## 2: 4.5 -0.09488366 24
## 3: 7.5 -0.12644513 24
## 4: 10.5 -0.11135073 24
## 5: 13.5 -0.07739952 24
## 6: 16.5 -0.06789986 24
## 7: 19.5 -0.07963820 24
## 8: 22.5 -0.09664082 24
## 9: 25.5 -0.04906552 24
## 10: 28.5 -0.08176457 24
## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.1329739 0.1356034 -0.04799946 159 0.0086 **
## 2: 4.5 0.2740341 0.2330713 0.02295544 159 0.35 :(
## 3: 7.5 0.3665768 0.3490692 0.01576858 159 0.59 :(
## 4: 10.5 0.3872417 0.4124680 -0.02740123 159 0.31 :(
## 5: 13.5 0.3980234 0.4736736 -0.08042557 159 0.0021 **
## 6: 16.5 0.4025157 0.4944921 -0.08989010 159 0.0011 **
## 7: 19.5 0.3737646 0.4911595 -0.11967220 159 1.8e-05 ***
## 8: 22.5 0.3827493 0.4974522 -0.11798555 159 8e-05 ***
## 9: 25.5 0.4016173 0.5042757 -0.10492584 159 0.00099 ***
## 10: 28.5 0.3773585 0.5179459 -0.14347107 159 1.5e-05 ***
## time error.diff shapes
## 1: 1.5 -0.04799946 24
## 2: 4.5 0.02295544 16
## 3: 7.5 0.01576858 16
## 4: 10.5 -0.02740123 16
## 5: 13.5 -0.08042557 24
## 6: 16.5 -0.08989010 24
## 7: 19.5 -0.11967220 24
## 8: 22.5 -0.11798555 24
## 9: 25.5 -0.10492584 24
## 10: 28.5 -0.14347107 24
Per group, motor task
##
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTM[niveau.group ==
## "bad"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.4018 -0.3136 -0.0293 0.2819 0.5717
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.16190 0.07389 2.191 0.0288 *
## timeNorm 0.04169 0.04874 0.855 0.3927
## obj.diff -0.71660 0.12152 -5.897 6.05e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.1121478)
##
## Null deviance: 75.138 on 629 degrees of freedom
## Residual deviance: 70.317 on 627 degrees of freedom
## AIC: 414.45
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.2879819 0.6028422 -0.3063430 63 2.4e-07 ***
## 2: 4.5 0.3560091 0.5311773 -0.1771179 63 0.00091 ***
## 3: 7.5 0.3514739 0.5237554 -0.1811341 63 0.003 **
## 4: 10.5 0.3219955 0.5051186 -0.1823421 63 0.00032 ***
## 5: 13.5 0.3492063 0.4887080 -0.1436574 63 0.0023 **
## 6: 16.5 0.3106576 0.4986379 -0.1967927 63 5.4e-05 ***
## 7: 19.5 0.2585034 0.4553692 -0.2043180 63 7.7e-05 ***
## 8: 22.5 0.3242630 0.4520935 -0.1492351 63 0.0034 **
## 9: 25.5 0.3446712 0.4503762 -0.1189400 63 0.025 *
## 10: 28.5 0.3446712 0.4851210 -0.1361192 63 0.0034 **
## time error.diff shapes
## 1: 1.5 -0.3063430 24
## 2: 4.5 -0.1771179 24
## 3: 7.5 -0.1811341 24
## 4: 10.5 -0.1823421 24
## 5: 13.5 -0.1436574 24
## 6: 16.5 -0.1967927 24
## 7: 19.5 -0.2043180 24
## 8: 22.5 -0.1492351 24
## 9: 25.5 -0.1189400 24
## 10: 28.5 -0.1361192 24
##
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTM[niveau.group ==
## "medium"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.5003 -0.3105 -0.0105 0.2884 0.6198
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.10023 0.04183 2.396 0.0167 *
## timeNorm 0.03586 0.03385 1.059 0.2896
## obj.diff -0.51350 0.07991 -6.426 1.92e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.1086791)
##
## Null deviance: 128.16 on 1139 degrees of freedom
## Residual deviance: 123.57 on 1137 degrees of freedom
## AIC: 710.11
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.3182957 0.4221507 -0.11161746 114 0.00051 ***
## 2: 4.5 0.3496241 0.4741037 -0.12734971 114 7.7e-05 ***
## 3: 7.5 0.3558897 0.4724383 -0.12056326 114 0.00085 ***
## 4: 10.5 0.3245614 0.4742501 -0.15291189 114 6.8e-06 ***
## 5: 13.5 0.3258145 0.4784144 -0.15372301 114 9.2e-06 ***
## 6: 16.5 0.3383459 0.4678202 -0.12867560 114 2e-04 ***
## 7: 19.5 0.3446115 0.4569733 -0.11111363 114 0.00033 ***
## 8: 22.5 0.3320802 0.4659744 -0.14627777 114 5e-05 ***
## 9: 25.5 0.3997494 0.4753022 -0.08100621 114 0.035 *
## 10: 28.5 0.3508772 0.4420746 -0.08130956 114 0.003 **
## time error.diff shapes
## 1: 1.5 -0.11161746 24
## 2: 4.5 -0.12734971 24
## 3: 7.5 -0.12056326 24
## 4: 10.5 -0.15291189 24
## 5: 13.5 -0.15372301 24
## 6: 16.5 -0.12867560 24
## 7: 19.5 -0.11111363 24
## 8: 22.5 -0.14627777 24
## 9: 25.5 -0.08100621 24
## 10: 28.5 -0.08130956 24
##
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTM[niveau.group ==
## "good"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.5243 -0.2887 0.0170 0.2472 0.6074
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.19432 0.03803 5.110 4.42e-07 ***
## timeNorm -0.16087 0.05554 -2.897 0.003919 **
## obj.diff -0.38512 0.10925 -3.525 0.000458 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.09099729)
##
## Null deviance: 56.480 on 569 degrees of freedom
## Residual deviance: 51.595 on 567 degrees of freedom
## AIC: 256.33
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.2305764 0.2345181 -0.022364755 57 0.59 :(
## 2: 4.5 0.3759398 0.3083734 0.059567775 57 0.083 .
## 3: 7.5 0.4335840 0.3598460 0.088313450 57 0.068 .
## 4: 10.5 0.4085213 0.3778445 0.026989430 57 0.57 :(
## 5: 13.5 0.3533835 0.3928473 -0.037906228 57 0.35 :(
## 6: 16.5 0.4185464 0.4250343 -0.003127012 57 0.94 :(
## 7: 19.5 0.3734336 0.5099102 -0.128686755 57 0.0067 **
## 8: 22.5 0.3859649 0.5363487 -0.151942393 57 0.0033 **
## 9: 25.5 0.3934837 0.5191794 -0.124405638 57 0.013 *
## 10: 28.5 0.2982456 0.4989451 -0.208899524 57 0.00012 ***
## time error.diff shapes
## 1: 1.5 -0.022364755 16
## 2: 4.5 0.059567775 16
## 3: 7.5 0.088313450 16
## 4: 10.5 0.026989430 16
## 5: 13.5 -0.037906228 16
## 6: 16.5 -0.003127012 16
## 7: 19.5 -0.128686755 24
## 8: 22.5 -0.151942393 24
## 9: 25.5 -0.124405638 24
## 10: 28.5 -0.208899524 24
Per group, sensory task
##
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTS[niveau.group ==
## "bad"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.7257 -0.2678 -0.1262 0.3162 0.7323
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.01988 0.03634 0.547 0.585
## timeNorm 0.04587 0.04484 1.023 0.307
## obj.diff -0.28844 0.06270 -4.600 4.99e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.1187679)
##
## Null deviance: 87.696 on 719 degrees of freedom
## Residual deviance: 85.157 on 717 degrees of freedom
## AIC: 514.24
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.2281746 0.3097399 -0.10902976 72 0.038 *
## 2: 4.5 0.3630952 0.4426004 -0.08561359 72 0.053 .
## 3: 7.5 0.3630952 0.4724575 -0.11685188 72 0.0061 **
## 4: 10.5 0.3392857 0.4525641 -0.12298405 72 0.0052 **
## 5: 13.5 0.3670635 0.4656216 -0.09769915 72 0.047 *
## 6: 16.5 0.4087302 0.4811146 -0.06977024 72 0.061 .
## 7: 19.5 0.3392857 0.4360550 -0.11830674 72 0.011 *
## 8: 22.5 0.4365079 0.4764586 -0.03047354 72 0.52 :(
## 9: 25.5 0.3769841 0.4251328 -0.04729018 72 0.27 :(
## 10: 28.5 0.3710317 0.4756434 -0.11448523 72 0.021 *
## time error.diff shapes
## 1: 1.5 -0.10902976 24
## 2: 4.5 -0.08561359 16
## 3: 7.5 -0.11685188 24
## 4: 10.5 -0.12298405 24
## 5: 13.5 -0.09769915 24
## 6: 16.5 -0.06977024 16
## 7: 19.5 -0.11830674 24
## 8: 22.5 -0.03047354 16
## 9: 25.5 -0.04729018 16
## 10: 28.5 -0.11448523 24
##
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTS[niveau.group ==
## "medium"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.70507 -0.22068 -0.04335 0.24408 0.76625
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.06325 0.02388 -2.648 0.00820 **
## timeNorm 0.08543 0.03269 2.613 0.00909 **
## obj.diff -0.23906 0.04135 -5.782 9.53e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.09501969)
##
## Null deviance: 111.35 on 1139 degrees of freedom
## Residual deviance: 108.04 on 1137 degrees of freedom
## AIC: 556.99
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.09899749 0.1876252 -0.11039497 114 1.2e-07 ***
## 2: 4.5 0.28446115 0.4616587 -0.18406741 114 2.4e-08 ***
## 3: 7.5 0.30325815 0.5145617 -0.21299544 114 2.4e-09 ***
## 4: 10.5 0.33583960 0.4960885 -0.16726069 114 2e-06 ***
## 5: 13.5 0.34085213 0.4349206 -0.09783900 114 0.00087 ***
## 6: 16.5 0.37468672 0.4700852 -0.09179220 114 0.0085 **
## 7: 19.5 0.41102757 0.5126837 -0.10061540 114 4.9e-06 ***
## 8: 22.5 0.39097744 0.5324474 -0.14712519 114 0.00012 ***
## 9: 25.5 0.44987469 0.5092968 -0.05935521 114 0.1 :(
## 10: 28.5 0.32957393 0.4942599 -0.17055244 114 9.2e-07 ***
## time error.diff shapes
## 1: 1.5 -0.11039497 24
## 2: 4.5 -0.18406741 24
## 3: 7.5 -0.21299544 24
## 4: 10.5 -0.16726069 24
## 5: 13.5 -0.09783900 24
## 6: 16.5 -0.09179220 24
## 7: 19.5 -0.10061540 24
## 8: 22.5 -0.14712519 24
## 9: 25.5 -0.05935521 16
## 10: 28.5 -0.17055244 24
##
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTS[niveau.group ==
## "good"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.70375 -0.19717 -0.02273 0.23861 0.72796
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.02461 0.04370 -0.563 0.574
## timeNorm -0.08556 0.06838 -1.251 0.212
## obj.diff -0.08977 0.07434 -1.208 0.228
##
## (Dispersion parameter for gaussian family taken to be 0.09043054)
##
## Null deviance: 24.581 on 269 degrees of freedom
## Residual deviance: 24.145 on 267 degrees of freedom
## AIC: 122.35
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.05291005 0.08093653 -0.05322518 27 0.0059 **
## 2: 4.5 0.16931217 0.26803793 -0.14051200 27 0.036 *
## 3: 7.5 0.38095238 0.48102915 -0.09893471 27 0.19 :(
## 4: 10.5 0.45502646 0.59775767 -0.15065745 27 0.052 .
## 5: 13.5 0.64021164 0.69702994 -0.05290538 27 0.32 :(
## 6: 16.5 0.46031746 0.60627919 -0.13542918 27 0.0082 **
## 7: 19.5 0.37037037 0.45492616 -0.09534505 27 0.16 :(
## 8: 22.5 0.40740741 0.54296476 -0.10429981 27 0.014 *
## 9: 25.5 0.37037037 0.51469153 -0.14376601 27 0.046 *
## 10: 28.5 0.35978836 0.54066115 -0.19060030 27 0.028 *
## time error.diff shapes
## 1: 1.5 -0.05322518 24
## 2: 4.5 -0.14051200 24
## 3: 7.5 -0.09893471 16
## 4: 10.5 -0.15065745 16
## 5: 13.5 -0.05290538 16
## 6: 16.5 -0.13542918 24
## 7: 19.5 -0.09534505 16
## 8: 22.5 -0.10429981 24
## 9: 25.5 -0.14376601 24
## 10: 28.5 -0.19060030 24
Per group, logical task
##
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTL[niveau.group ==
## "bad"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.50249 -0.31192 0.00158 0.27300 0.57238
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.18310 0.05115 3.580 0.000366 ***
## timeNorm -0.01421 0.04068 -0.349 0.726902
## obj.diff -0.58809 0.08998 -6.536 1.17e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.1006514)
##
## Null deviance: 79.537 on 749 degrees of freedom
## Residual deviance: 75.187 on 747 degrees of freedom
## AIC: 411.33
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.3333333 0.4716057 -0.14568003 75 0.00049 ***
## 2: 4.5 0.4057143 0.4928915 -0.09166913 75 0.041 *
## 3: 7.5 0.4361905 0.5070220 -0.06921125 75 0.1 :(
## 4: 10.5 0.3466667 0.4876115 -0.14592638 75 0.00062 ***
## 5: 13.5 0.3885714 0.4705744 -0.08546184 75 0.042 *
## 6: 16.5 0.3809524 0.4634084 -0.08072180 75 0.037 *
## 7: 19.5 0.3523810 0.4739411 -0.13894639 75 0.0027 **
## 8: 22.5 0.3142857 0.4296825 -0.12873142 75 0.0014 **
## 9: 25.5 0.3561905 0.4234421 -0.07931452 75 0.063 .
## 10: 28.5 0.3619048 0.4376259 -0.07920488 75 0.068 .
## time error.diff shapes
## 1: 1.5 -0.14568003 24
## 2: 4.5 -0.09166913 24
## 3: 7.5 -0.06921125 16
## 4: 10.5 -0.14592638 24
## 5: 13.5 -0.08546184 24
## 6: 16.5 -0.08072180 24
## 7: 19.5 -0.13894639 24
## 8: 22.5 -0.12873142 24
## 9: 25.5 -0.07931452 16
## 10: 28.5 -0.07920488 16
##
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTL[niveau.group ==
## "medium"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.6842 -0.3690 0.0772 0.3315 0.5546
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.22719 0.03565 6.373 3.31e-10 ***
## timeNorm 0.12666 0.04933 2.568 0.0104 *
## obj.diff -0.59514 0.07771 -7.659 6.09e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.1277274)
##
## Null deviance: 99.075 on 719 degrees of freedom
## Residual deviance: 91.581 on 717 degrees of freedom
## AIC: 566.61
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.2242063 0.2095827 -0.02888658 72 0.68 :(
## 2: 4.5 0.4305556 0.3149495 0.11381852 72 0.0088 **
## 3: 7.5 0.4166667 0.4085764 0.01184582 72 0.84 :(
## 4: 10.5 0.4880952 0.4423093 0.05303055 72 0.34 :(
## 5: 13.5 0.5059524 0.4224978 0.09425537 72 0.053 .
## 6: 16.5 0.5218254 0.4587626 0.05950609 72 0.14 :(
## 7: 19.5 0.5099206 0.4752146 0.04435498 72 0.43 :(
## 8: 22.5 0.4821429 0.4348125 0.04340068 72 0.23 :(
## 9: 25.5 0.4821429 0.4639661 0.02021695 72 0.66 :(
## 10: 28.5 0.5257937 0.4719986 0.07500839 72 0.15 :(
## time error.diff shapes
## 1: 1.5 -0.02888658 16
## 2: 4.5 0.11381852 24
## 3: 7.5 0.01184582 16
## 4: 10.5 0.05303055 16
## 5: 13.5 0.09425537 16
## 6: 16.5 0.05950609 16
## 7: 19.5 0.04435498 16
## 8: 22.5 0.04340068 16
## 9: 25.5 0.02021695 16
## 10: 28.5 0.07500839 16
##
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTL[niveau.group ==
## "good"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.52214 -0.26501 -0.02093 0.25379 0.68368
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.13267 0.02547 5.209 2.46e-07 ***
## timeNorm 0.17621 0.05107 3.451 0.00059 ***
## obj.diff -0.72141 0.07276 -9.915 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.09487992)
##
## Null deviance: 81.624 on 749 degrees of freedom
## Residual deviance: 70.875 on 747 degrees of freedom
## AIC: 367.04
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.08761905 0.08010825 -0.052761161 75 0.023 *
## 2: 4.5 0.23428571 0.16325372 0.044004349 75 0.22 :(
## 3: 7.5 0.31047619 0.29337315 0.004158512 75 0.93 :(
## 4: 10.5 0.34666667 0.37207770 -0.026222602 75 0.49 :(
## 5: 13.5 0.34476190 0.45469333 -0.114025675 75 0.0029 **
## 6: 16.5 0.36952381 0.50703661 -0.142309890 75 0.0015 **
## 7: 19.5 0.37523810 0.48995306 -0.127835397 75 0.0033 **
## 8: 22.5 0.37142857 0.45150631 -0.089772558 75 0.072 .
## 9: 25.5 0.41904762 0.48919916 -0.072377500 75 0.15 :(
## 10: 28.5 0.44380952 0.52420913 -0.082245022 75 0.15 :(
## time error.diff shapes
## 1: 1.5 -0.052761161 24
## 2: 4.5 0.044004349 16
## 3: 7.5 0.004158512 16
## 4: 10.5 -0.026222602 16
## 5: 13.5 -0.114025675 24
## 6: 16.5 -0.142309890 24
## 7: 19.5 -0.127835397 24
## 8: 22.5 -0.089772558 16
## 9: 25.5 -0.072377500 16
## 10: 28.5 -0.082245022 16
Link between d(t-1) and error
##
## Call:
## glm(formula = error.subj.diff.mise ~ est.confidence.norm, data = DTM)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.69707 -0.28435 -0.01684 0.28245 0.79371
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.04093 0.01847 -2.217 0.0267 *
## est.confidence.norm -0.14331 0.03231 -4.436 9.59e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.1121776)
##
## Null deviance: 264.48 on 2339 degrees of freedom
## Residual deviance: 262.27 on 2338 degrees of freedom
## AIC: 1525.5
##
## Number of Fisher Scoring iterations: 2
##
## Call:
## glm(formula = error.subj.diff.mise ~ est.confidence.norm, data = DTS)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.83760 -0.21568 -0.02533 0.23895 0.82506
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.12278 0.01774 -6.919 5.99e-12 ***
## est.confidence.norm 0.02176 0.03276 0.664 0.507
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.1054863)
##
## Null deviance: 224.52 on 2129 degrees of freedom
## Residual deviance: 224.47 on 2128 degrees of freedom
## AIC: 1257.9
##
## Number of Fisher Scoring iterations: 2
##
## Call:
## glm(formula = error.subj.diff.mise ~ est.confidence.norm, data = DTL)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.85522 -0.27519 -0.02402 0.27775 0.80100
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.02339 0.01675 -1.396 0.163
## est.confidence.norm -0.02606 0.03348 -0.778 0.436
##
## (Dispersion parameter for gaussian family taken to be 0.1210069)
##
## Null deviance: 268.47 on 2219 degrees of freedom
## Residual deviance: 268.39 on 2218 degrees of freedom
## AIC: 1615.7
##
## Number of Fisher Scoring iterations: 2## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: error.subj.diff.mise ~ est.confidence.norm + (1 | IDjoueur)
## Data: DTAll
##
## REML criterion at convergence: 3391.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.06594 -0.71512 -0.03989 0.71176 3.10382
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 0.02224 0.1491
## Residual 0.09362 0.3060
## Number of obs: 6690, groups: IDjoueur, 80
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.05191 0.01942 128.00000 -2.673 0.008503 **
## est.confidence.norm -0.07215 0.01859 6685.00000 -3.881 0.000105 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## est.cnfdnc. -0.473
## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: error.subj.diff.mise ~ est.confidence.norm + (1 | IDjoueur)
## Data: DTM
##
## REML criterion at convergence: 446.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.9914 -0.6545 0.0126 0.5910 3.1773
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 0.04937 0.2222
## Residual 0.06347 0.2519
## Number of obs: 2340, groups: IDjoueur, 78
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.06000 0.03076 153.00000 -1.951 0.052890 .
## est.confidence.norm -0.10732 0.03191 2337.70000 -3.363 0.000782 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## est.cnfdnc. -0.550
## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: error.subj.diff.mise ~ est.confidence.norm + (1 | IDjoueur)
## Data: DTS
##
## REML criterion at convergence: 1102.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.60773 -0.66645 -0.04568 0.67995 2.81924
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 0.01313 0.1146
## Residual 0.09256 0.3042
## Number of obs: 2130, groups: IDjoueur, 71
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.164e-01 2.411e-02 3.078e+02 -4.827 2.19e-06 ***
## est.confidence.norm 8.855e-03 3.778e-02 1.710e+03 0.234 0.815
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## est.cnfdnc. -0.779
## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: error.subj.diff.mise ~ est.confidence.norm + (1 | IDjoueur)
## Data: DTL
##
## REML criterion at convergence: 1105.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2414 -0.7062 -0.0089 0.6856 3.1920
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 0.03321 0.1822
## Residual 0.08827 0.2971
## Number of obs: 2220, groups: IDjoueur, 74
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -3.217e-02 2.779e-02 1.680e+02 -1.158 0.249
## est.confidence.norm -6.521e-03 3.749e-02 2.154e+03 -0.174 0.862
##
## Correlation of Fixed Effects:
## (Intr)
## est.cnfdnc. -0.606